1. Field of the Invention
The present invention relates to the analysis of statistical data, preferably on a computer and using a computer implemented program. The invention more specifically relates to a method and apparatus that accurately analyzes statistical data when that data is not “normally distributed,” by which is meant, as used herein, that the data set does not correspond to a “normal probability distribution” or does not show a bell-shaped curve.
2. Description of the Prior Art
Conventional data analysis involves the testing of statistical hypotheses for validation. The usual method for testing these hypotheses, in most situations, is based on the well known “General Linear Model,” which produces valid results only if the data are either normally distributed or approximately so.
Where the data set to be analyzed is not normally distributed, the known practice is to transform the data by non-linear transformation to comply with the assumptions of most statistical tests. This practice is disclosed in, for example, Haoglin, Mosteller, Tukey, UNDERSTANDING ROBUST AND EXPLORATORY DATA ANALYSIS (1977), which is incorporated herein by reference. It was previously thought that data could be transformed to comply with known distributional assumptions without affecting the integrity of the analysis. More recent research has demonstrated, however, that the practice of non-linear transformation actually introduces unintended and significant error into the analysis. See, e.g., Terrence B. Peace, Ph.D, TRANSFORMATION AND CORRELATION (2000) and TRANSFORMATION AND T-TEST (2000), which is incorporated herein by reference. A solution to this problem is needed. The subject invention therefore provides a method and apparatus capable of evaluating statistical data and outputting reliable analytical results without relying on transformation techniques.
U.S. Pat. No. 5,893,069 to White, Jr., entitled “System and method for testing prediction model,” discloses a computer implemented statistical analysis method to evaluate the efficacy of prediction models as compared to a “benchmark” model. White discloses the “bootstrap” method of statistical analysis in that it randomly generates data sets from the empirical data set itself.